* <\/sup>LNEC \u2013 National Laboratory for Civil Engineering, Lisbon, Portugal\n<\/p>\n\n\n\n ava@lnec.pt<\/a>\n<\/p>\n\n\n\n mjoaofalcao@lnec.pt<\/a>\n<\/p>\n\n\n\n asalvado@lnec.pt<\/a>\n<\/p>\n\n\n\n ABSTRACT<\/strong>\n<\/p>\n\n\n\n \n As infrastructures age and urban populations grow, maintaining structural integrity and operational efficiency has become an increasingly pressing challenge. Traditional maintenance practices, often based on pre-defined theoretical plans, can result in inefficient resource allocation, higher costs, and an increased risk of unforeseen failures. Digital Twin technology provides a dynamic virtual representation of physical assets, enabling real-time simulations, condition-based forecasting, and scenario analysis. When performance data is available (collected from integrated infrastructure monitoring), this approach allows for continuous tracking of key indicators related to the structural service life of assets. This represents a significant evolution in infrastructure management, supporting more informed and strategic decision-making. It enables the adoption of a proactive maintenance approach, improving operational efficiency, reducing risks, and extending the lifespan of urban infrastructure. The integration of maintenance strategies with Digital Twin technology also aligns with sustainability principles by minimizing resource waste and reducing emissions associated with emergency corrective interventions.\n<\/p>\n\n\n\n \n This paper proposes the integration of maintenance strategies with Digital Twin technology to enhance asset and facility management in the Architecture, Engineering, Construction, and Operation (AECO) sector, contributing to the advancement of building and infrastructure management and positions emerging technologies as essential tools for future-ready cities. The study outlines implementation strategies, performance metrics, and scalability considerations applicable to a wide range of infrastructure systems.\n<\/p>\n\n\n\n KEYWORDS <\/strong>\n<\/p>\n\n\n\n \n Maintenance, Digital twin, smart building management\n<\/p>\n\n\n\n \n The rapid growth of urban populations and the resulting increase in infrastructure demand have intensified the challenges of maintenance and asset management in the Architecture, Engineering, Construction, and Operations (AECO) sector [1]. As existing infrastructures age, ensuring structural integrity and operational efficiency has become a priority for both public and private stakeholders [2,3,4]. Traditional maintenance methods, often based on theoretical plans or fixed schedules, are limited in addressing complexity and unpredictability of asset lifecycles. This can lead to inefficient resource allocation, high costs, and a higher risk of unplanned failures [5,6,7,8]. In this context, emerging technologies offer new possibilities for transforming urban asset management. Among these, the Digital Twin stands out. It is defined as a dynamic virtual representation of a physical asset, capable of reproducing its operating conditions in real time through the integration of sensors data, computational models, and simulation algorithms. Unlike static models, Digital Twins enable continuous monitoring of key performance indicators, forecasting based on real conditions, and degradation analysis or failure scenarios [9,10]. The integration of maintenance strategies and Digital Twin technology represents a significant advance in the AECO sector, enabling the transition from reactive practices to proactive and predictive approaches. This translates into greater operational efficiency, risk mitigation, and extended asset lifespans. Moreover, this integration aligns with sustainability principles by reducing resource waste and minimizing emissions associated with emergency and corrective interventions [11,12,13].\n<\/p>\n\n\n\n \n This paper examines the opportunities and challenges of incorporating Digital Twins into maintenance processes, discussing implementation strategies, performance metrics, and scalability possibilities. The aim is to contribute to the advancement of asset management in the AECO sector, positioning the Digital Twin as a strategic tool for the development of resilient and future-ready cities.\n<\/p>\n\n\n\n \n Asset maintenance is a critical component of infrastructure management, directly influencing reliability, safety, and performance throughout the lifecycle. Traditionally, maintenance models fall into three main categories: corrective, preventive, and predictive [14,15,16,17]. Corrective maintenance consists of repairing failures only after they occur. Although straightforward, this approach carries high risks, as unexpected failures can generate significant costs and compromise the safety of users and operations [15,18,19]. Preventive maintenance seeks to reduce the likelihood of failures through scheduled inspections and interventions. Although this increases system reliability, it is not always efficient, since it may result in unnecessary replacements and additional costs [16,20]. More recently, predictive maintenance has gained prominence. Based on continuous asset monitoring it uses performance data to forecast failures before they occur. This approach is particularly relevant when combined with digital technologies, smart sensors, and real-time data analysis [15,16,21].\n<\/p>\n\n\n\n \n The term Digital Twin was originally introduced in the context of the product lifecycle management, and later expanded to multiple sectors, including manufacturing, healthcare, energy, and infrastructure. A Digital Twin is defined as a digital replica of a physical system, continuously updated through the integration of data collected in real time [22]. Its main characteristic is its dynamic nature: instead of representing a static model, it reflects the behaviour and actual state of the asset over time. This bidirectional connection between the physical and virtual systems enables advanced simulations, failure prediction, scenario analysis, and decision support [23]. In the AECO sector, Digital Twins are often associated with Building Information Modelling (BIM), which provides the geometric and informational basis of assets. However Digital Twins evolve beyond BIM by integrating real-time data from sensors and monitoring systems. This integration enables a continuous cycle of information collection, analysis, and feedback, supporting intelligent infrastructure and buildings management [24].\n<\/p>\n\n\n\n \n The integration of maintenance strategies with Digital Twins is an iterative process as represented in Figure 1. The adoption of Digital Twins in urban infrastructure maintenance has expanded in recent years, driven by the need to optimize resources, reduce unexpected failures, and support sustainable practices. Combined with IoT sensors, they allow real-time monitoring of critical parameters such as vibration, temperature, structural deformations, and energy consumption [22,24]. Case studies highlight their application in bridges, highways, railways, and smart buildings. For example, Digital Twins have been used to monitor the structural integrity of bridges in real time, enabling early interventions and extending the infrastructure’s lifespan. Similarly, in commercial buildings, they have been used to optimize HVAC and energy systems, resulting in significant reductions in operating costs [23].\n<\/p>\n\n\n\n <\/p>\n\n\n\n \n Figure 1 \u2013 Integrating maintenance and digital twin\n<\/p>\n\n\n\n \n Despite these advances, challenges remain for large scale adoption, namely: i) interoperability between different platforms and data standards; ii) high initial costs for implementing sensors, software, and digital infrastructure; and iii) the need for professional training to interpret complex data and make informed decisions based on digital models. Nevertheless, Digital Twins are expected to consolidate itself as a key technology in asset management, mainly due to its ability to integrate maintenance, operation, and sustainability into a single digital ecosystem [22,23,24].\n<\/p>\n\n\n\n \n The proposed methodology follows an asset-centric, risk-oriented approach, supported by emerging digital technologies, with emphasis on integrating maintenance strategies and Digital Twin modelling. This process is developed in progressive stages, involving data collection, virtual model construction, scenario simulation, and decision-making support [10,22,24]. At its core, the methodology establishes a continuous cycle of integration between the actual performance of urban assets and their digital representation. This cycle consists of four steps (Figure 2).\n<\/p>\n\n\n\n <\/p>\n\n\n\n \n Figure 2 \u2013 Integration between the actual performance of urban assets and their digital twin\n<\/p>\n\n\n\n \n Through this approach, maintenance evolves from reactive or preventative to predictive and proactive, guided by digitally identified evidence and risks [10,11,15]. Table 1 describes the steps for implementing and operationalizing the methodology.\n<\/p>\n\n\n\n1. INTRODUCTION<\/strong><\/h2>\n\n\n\n
2. FRAMEWORK<\/strong>\n<\/h2>\n\n\n\n
2.1 Maintenance<\/h3>\n\n\n\n
2.2 Digital Twin<\/h3>\n\n\n\n
2.3 Integrating Maintenance and Digital Twin<\/h3>\n\n\n\n
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<\/figure>\n\n\n\n3. METHODOLOGY<\/strong>\n<\/h2>\n\n\n\n
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